Low Cost Microcontroller Based Implementation of Robust Voltage Based Capacitor Charge Balance Control Algorithm
Bibliographic record
Abstract
In this paper, a low cost microcontroller based control method utilizing the concept of capacitor charge balance is presented to achieve a near-optimal transient response for Buck converters. First, this paper presents a new derivation of practical charge balance equations based on simplified differential equations. The final implementation does not require complex calculations and accurate knowledge of the output filter LC parameter. The hardware implementation only requires the output voltage information so that no extra sensing circuitry is needed compared with voltage mode controller. Also, this algorithm can be simply extended to adaptive voltage positioning (AVP) application. Second, due to the simplicity of this algorithm, a low cost microcontroller unit (MCU) based controller can be implemented to shorten the developing period for users. Thirdly, unlike previous work, the proposed voltage based CBC (V-CBC) controller does not require accurate current sensor or fast analog-to-digital converter (ADC). Instead, to detect the critical time instant when the inductor current equals the new load current, a practical extreme voltage detector is introduced to capture the output voltage peak/valley information. Experimental prototype is built to verify the feasibility and advantage of the new method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".